AI has become a game changer in many fields, and retail is no exception. In fact, 73% of retailers are already using AI, and 15% more are discussing AI implementation in the next year.

Should you join them immediately? Artificial intelligence is a powerful tool, but it is not for every business. Check if your team and company are prepared for innovations before jumping in. 

In this article, we share ten tips from our AI/ML specialists, based on one of the real-life AI solutions for retail we worked on. So, let us dive into the context!

This article is a part of the “AI in retail” series where we showcase all aspects of AI implementation for small and mid-sized businesses. Read more materials from this series:

• How retailers benefit from AI innovations;
• AI in retail trends and challenges to solve;
• AI use cases for the e-commerce niche;
• AI implementation in physical stores;
• How to choose between custom and ready-made AI for retail?

How We Prepared and Developed an Advanced Recommendation Engine for Retail

Our client, a top US health & wellness retailer with a robust online platform and nationwide store network, approached us to boost their digital and in-store customer service.

Our collaboration resulted in a feature-rich mobile app that acts as a smart personal assistant for shoppers. We incorporated AR/VR, face recognition technology, and an interactive GPT-powered chatbot with advanced AI analytics.

Sounds complicated, right? No worries, we will explain all retail software features in detail.

• Virtual try-on: Users can test products online with AR capabilities.
• Personal recommendation system: The app offers the most relevant products and deals based on customer data.
• The store locator helps people check business hours and product stocks in nearby stores.
• Shop assistant handles online purchases and proceeds payments.
• Order tracker: a chatbot helps shoppers monitor orders and shipments.
• Expert tips: The feature provides tutorials and insights that help shoppers with their product choices.
• The help center assists with common questions and receives complaints.

The final product took over two years of dedication, incorporating various AI applications in retail to enhance its functionality. Find more details in our case study.

But any great project, including this one, starts with the preparation stage. Successfully adopting smart technologies requires more than just a decision. Wondering what else is necessary? Let us help you figure it out!

How to Prepare for Artificial Intelligence Solutions for Retail: Tips from CHI Software AI/ML Team

Setting up AI involves careful planning. Developers should know your expectations for an algorithm. And you, as a product owner, should be aware of the steps and data you need to achieve success.

Our engineers have listed essential steps for AI setup and included examples from our case study for even better understanding.

What to Consider Before AI Implementation

1. Business Goals 

Implementing artificial intelligence retail solutions can transform your business only if innovations match your vision and objectives. Without such clarity, the algorithm’s strengths might remain unused. So start with identifying a key problem and estimate how it affects your business. After that, you can set goals for what you want to achieve with AI and calculate the expected ROI.

Our retail client established clear business goals and expected results from every feature of our beauty mobile app project.For example, a virtual makeup simulator was designed to boost engagement, help shoppers with their decisions, and ultimately drive sales. This approach ensured everyone on the team could quickly grasp the client’s vision and stay on track.

2. Realistic Expectations for Using AI in Retail

While AI is powerful, it is not a cure-all. Like any tool, it has pros and cons and needs considerable time and effort to function as intended. First, algorithm results may not meet expectations at 100% of attempts. So, it is vital to assess the AI adoption process realistically and map out all potential scenarios.

Early attempts at chatbot communication on our project were not always spot-on for shoppers. For instance, once, the chatbot recommended an “invisible lipstick” (referring to a nude-colored lipstick) with the cheeky comment, “Perfect for those days when you want your lips to blend in with everything!” A laughable moment! The algorithm improved fast, and we were grateful our client understood its learning curve.

3. Budget and Timeline

Building retail AI software will undoubtedly require time and investment; that’s a given. But how much exactly? To know for sure, AI engineers should evaluate the resources – both needed and available, such as:

• hardware, 
• software, 
• labor, 
• time for model training and maintenance, 
• indirect costs like legal fees for compliance.

Accurate estimation itself takes time, too. Diligent preparation of AI projects will save you from unplanned budget spikes down the road. So, be patient and give the engineers their time.

4. Data 

Data fuels your project. It is essential for initial model training and further improvements. Let AI/ML experts do their job and evaluate your data to ensure:

• Volume: Is there enough data for AI to use?
• Quality: Is the data accurate and labeled properly?
• Bias: Does the data provide a fair representation?
• Privacy: Have you obtained the necessary permissions to gather and use the data?

5. Skills and Expertise

One in five retailers identify a knowledge gap as a significant barrier to AI adoption. Make sure your team has the required expertise for AI integration, or consider partnering with specialists. Additionally, take care of personnel training or recruiting experts for successful AI implementation.

6. Regulations

Ensure your future AI implementation strategy complies with data privacy and security regulations in your country and domain. So, our mobile app is designed to comply with US regulations of artificial intelligence. 

7. Smooth Integration

Intelligent tools need to mesh seamlessly with your current systems. Evaluate their integration with your infrastructure and assess the impact on workflows and operations. 

With the introduction of the virtual makeup try-on feature, the demand for product testers at our client’s stores decreased. This tendency should be reflected in the upcoming allocations for in-store testers.

8. Change Management

The survey found that 37% of employees worry intelligent tech might limit their career growth, and 69% fear it might replace them entirely. It is vital to talk openly to your team about AI changes and uncover emerging opportunities to focus on creative work rather than boring repetitive tasks. 

9. Scalability

It is a good idea to start small when it comes to AI. One store, a product category, or a feature are enough to try out new capabilities. But at the same time, have an eye on the AI implementation roadmap and plan your scaling strategy early to prevent future inefficiencies and bottlenecks. 

We started with the virtual makeup try-on feature on our AI project. Only after assessing initial findings, we incorporated a personal recommendation system and gradually expanded to a broader range of features. Slow and steady wins the race.

10. Further Maintenance

Artificial intelligence is a never-ending journey in some way. Your AI project will require regular monitoring, data enrichment, and model fine-tuning. 

Thus, our client’s app is live now, but we are constantly refining features, training algorithms, and exploring new additions.

To Sum Up

Now you have it. Didn’t we say preparation was essential? Alexander Graham Bell once said, “Before anything else, preparation is the key to success.” This statement fully describes the preparation for AI projects. But no worries! You are not on your own in it. Trust people who work with algorithms daily. Speaking of which, do you know any?

We at CHI Software have a dedicated department of AI/ML engineers who do not just know AI; they are always on the pulse of the latest trends and technologies. Actually, I am among them and would be happy to help you. Shall we talk?

A single conversation is already your first step towards new opportunities. It does not sound overwhelming now, does it? So, let’s begin! 

FAQs

• What are some common AI use cases in retail?

  They include personalized product recommendations using machine learning algorithms, customer service chatbots, inventory management through predictive analytics, and in-store navigation aids. Artificial intelligence in the retail industry is also used for price optimization, sales forecasting, and security enhancements with facial recognition technologies.
  

• What challenges do retailers face when implementing AI-powered shopping solutions?

  Сhallenges involve integrating new AI solutions for retail with existing software, handling large datasets necessary for AI, and ensuring customer data privacy. Additional challenges include staying compliant with evolving regulations and managing expectations for AI interactions.

• What should businesses consider before investing in AI solutions on the retail market?

  Businesses should evaluate their current infrastructure's compatibility with AI, understand what retail software solutions they need, and assess the quality of their data. It is also important to consider the company's strategic goals, customer experience objectives, and any potential ethical implications of using AI.
  

• Is my retail business ready for AI implementation?

  To determine if your business is prepared for AI implementation, assess whether your operations can adapt to and integrate artificial intelligence solutions for retail. Review if you have the required data infrastructure, talent, and resources to manage the transition. Ensure that there is a strategic alignment with your business goals and a readiness to invest in necessary retail software solutions.

• How should retailers plan a budget and timeline for AI implementation?

  Retailers should first determine the scope of their AI solution. This includes upfront development costs, training, system integration, and potential operational disruptions. Retailers should also account for ongoing expenses such as software updates, maintenance, and data management to ensure the sustainability of their AI-powered retail software solutions.